Machine Learning Product Management: A Practical Guide

Examine high-profile machine learning failures—Watson for Oncology, Tay, Face ID, and Rekognition—to understand why 85% of ml projects fail and when to apply ml.

Assess when to use machine learning by evaluating data quality and sufficiency, problem complexity beyond traditional programming, and scalability; consider personalization and time-varying problems as strong ML candidates.

Identify scenarios to avoid machine learning, including cases needing 100% accuracy or full interpretability, or where rule-based solutions suffice. Learn how probability, explainability, and risk shape deployment.

Assess when machine learning models require explainability across four use cases, from recommendations to image tagging, and learn to justify decisions through legal, financial, and ethical lenses.

Assess data usability before applying machine learning by checking availability, privacy, security, and legal constraints, and evaluating quality through relevancy, freshness, completeness, scarcity, and bias.

Explore how to evaluate when to apply machine learning to real-world use cases, considering complexity, time-varying factors, accuracy, explainability, ethics, and data bias to justify decisions.

ML product managers add data as a core dimension beyond technology, UX, and business, balancing data collection, quality, security, and collaboration with third parties.

Explore three approaches to organizing machine learning teams: functional, decentralized with full-stack product teams, and a mixed model, weighing pros like faster cycles, knowledge sharing, and collaboration overhead.

Identify the three key roles in an ML team: data engineer, data scientist, and ML engineer, and how they collaborate through data infrastructure, feature stores, and a production pipeline.

Formulate the business need, define minimum viable product and success metrics, then prototype through data access, labeling, feature engineering, model training, evaluation, and deploy batch or real-time predictions with monitoring.

Identify a potential use case for your product, assess if machine learning fits it, and test risky assumptions with a low-cost plan to validate your hypothesis.

Frame your machine learning problem by defining quantitative goals, success and failure criteria, model outputs for time to prepare, and when these outputs are available for dispatch decisions.

Formulate machine learning problems by selecting between binary and multi-class classification, and single or multi-label variants; choose uni dimensional and multi dimensional regression, then define the problem statement and metrics.

Explore how Google's reCAPTCHA uses user labeling to generate training data for supervised machine learning, enabling image annotation, digitization, and improvements to maps and self-driving cars.

Explore real-world user-generated data labeling used to improve machine learning models, with Google reCAPTCHA and Grammarly as examples.

Begin by simplifying the problem to binary classification or regression, such as predicting video popularity 28 days after upload, and use a simple baseline to justify any complexity.

Select 1–3 easy-to-obtain features available at prediction time, and design a simple data table and pipeline, illustrated by Uber Eats with average prep time, order size, and time of day.

Explore open data sources such as Kaggle, Amazon datasets, UCI, Google datasets, government and computer vision collections, with usage examples and licensing notes.

Garbage in, garbage out applies to machine learning; measure data quality, ensure at least ten times as many data points as features, and cover a full range of feature combinations.

Explore how data is stored across databases, warehouses, lakes, and graph databases, and how scalability, accessibility, latency, and throughput shape machine learning workloads.

Explore data scrubbing techniques to refine datasets, including feature selection, deduplication, and feature reduction; understand one-hot encoding for categorical data and strategies for missing values.

Select an appropriate sampling granularity based on the prediction task and features, and address data quality through filtering, handling imbalanced data, and a reproducible train-test split.

Master transforming data and feature engineering, including numeric normalization, bucketing, and quality transforms like tokenization and lowercasing. Decide whether to transform data before training or inside the model.

Explore feature engineering by creating useful features and establishing a baseline to judge their impact on model predictions. Brainstorm, create, and test features, using domain knowledge to improve results.

Brainstorm two new feature candidates to improve a machine learning model, exploring signals beyond the initial features, such as food type from menu data and a weekend indicator.

Discover how data scientists choose among four directions: classical, reinforcement, ensemble methods, and deep learning, by comparing algorithms and their use for regression, classification, clustering, and anomaly detection.

Weigh building, outsourcing, or buying your ML solution by core strategy and ROI while evaluating data, specialization, integration, customization, security, price, and ML as a service options.

Explore MLaaS across AWS and Google Cloud, including Polly, SageMaker, Lex, Rekognition, Comprehend, Transcribe, Vertex AI, AutoML, and Dialogflow.

Explore regression and classification through linear, polynomial, and logistic regression, including simple and multiple linear regression, regression lines (hyperplanes), and probability-based thresholds for binary outcomes.

Explore how support vector machines maximize the margin to classify data, juxtaposed with logistic regression, and compare nonparametric methods like k-nearest neighbors and decision trees, including entropy and information gain.

Explore unsupervised learning through clustering with k-means and mean shift, learning how centroids, k values, and density-based windows group data, converge, and reveal structure.

Explore anomaly detection techniques, focusing on local outlier factor and DBSCAN, to identify outliers through local density and density-based clustering. Understand core, border, and noise points and parameter choices.

Ensemble methods combine predictions from multiple models to improve performance, using bagging, boosting, and stacking with parallel or sequential base learners such as random forest and gradient boosting.

Explore why accuracy can be misleading and learn how the confusion matrix clarifies true positives, false positives, true negatives, and false negatives in imbalanced datasets.

Explore how to compute accuracy, error rate, recall (sensitivity), precision, specificity, and F1 score from a confusion matrix, and understand the trade-off between precision and recall using practical examples.

Apply a confusion matrix to identify true positives, true negatives, false positives, and false negatives, then compute precision and recall using practical examples.

Use a confusion matrix to compare precision, recall, and F1 for model selection. See how Uber Eats and diagnosing cancer illustrate optimizing for different UX goals.

Determine which metric to optimize: precision, recall, or F1 score, by weighing false positives and false negatives across satellite launches, fraud detection, spam filtering, copyright, and search results.

Explore deployment methods for machine learning models, including real-time inference, batch inference, and edge deployment, and consider latency, data privacy, network connectivity, and cost when selecting a method.

Monitor deployed models as a live system by tracking data quality, data drift, concept drift, and model performance using Grafana and Great Expectations, with alerting and on-call practices.

Celebrate finishing the course and boost your confidence as a machine learning product manager, then continue studying, apply your learnings, and engage in the Q&A.